Facial Competence & Vote Share

Ella Foster-Molina

Univariate Linear Regressions

vote share
(1) (2)
facial competence 0.660***
(0.127)
post 2003 0.088
(0.054)
Constant -0.312*** -0.039
(0.066) (0.038)
Observations 119 119
Adjusted R2 0.180 0.014
Note: p<0.05; p<0.01; p<0.001

Multiple Regression Plane

Hover over a point to see which state that election was held in.

vote share
(1) (2) (3)
facial competence 0.660*** 0.673***
(0.127) (0.126)
after 2003 0.088 0.101*
(0.054) (0.048)
Constant -0.312*** -0.039 -0.369***
(0.066) (0.038) (0.071)
Observations 119 119 119
Adjusted R2 0.180 0.014 0.203
Note: p<0.05; p<0.01; p<0.001

Interaction Terms (ie somewhat nonlinear regression surfaces)

This is just for fun, demonstrating what’s called an interaction term. Notice that the green and purple lines are no longer parallel to each other. Interaction terms in a regression allow different slopes for different crosssections of the regression surface. In stats terminology, the marginal effect (slope, kind of) of facial competence can be different after 2003 than it is before 2003 when an interaction term is used in the regression. These are also called heterogeneous effects (different effects for different groups).